1. Field of the Invention
The present invention relates to EEG monitoring systems. The invention, in particular relates to EEG monitoring systems, of the types which can be continuously carried by a person being monitored. More specifically, it relates to analog-to-digital input signal converters for signals from electrodes or transducers measuring EEG signals. The invention further relates to a method of converting an analog signal to a digital signal in an EEG monitoring system.
An analog-to-digital converter, denoted an A/D converter in the following, converts a varying current or voltage into a digital data format. Several different A/D converter topologies exist, each having benefits and tradeoffs in terms of conversion speed, accuracy, quantization noise, current consumption, word length, linearity and circuit complexity. In contemporary, digital hearing aid designs, the delta-sigma A/D converter type is the preferred converter type due to a number of important factors such as easy aliasing filter implementation, conversion noise being controllable by design, comparatively low power consumption and relatively easy implementation due to a low component count when compared to existing A/D converter designs.
By definition, noise inherent in a signal processing device is unwanted signals introduced by the signal processing device itself. Inherent noise may e.g. originate from inadequate operating conditions, poor design or variations in component values. These circumstances have to be taken into account in designing the signal processing device. In A/D converters, several different types of noise may be observed. Among these are conversion noise, quantization noise, thermal noise, flicker noise, recombination noise, and noise due to various physical limitations in the gain-producing elements. In order to provide a distinction between the sources of these different noise types, the most important noise types will be discussed briefly in the following.
Quantization noise originates from the process of quantifying a continuous input voltage span into a finite set of voltage levels that may be represented by discrete, binary levels according to the expression:LN=2n where LN is the number of discrete levels possible and n is the number of bits used to represent a single sample in the digital domain. Quantization noise may be thought of as the difference between the actual input voltage of a single sample and the discrete voltage used to represent it. This type of noise may thus be minimized, e.g. by increasing the number of bits representing the signal arbitrarily, and will therefore not be discussed further here.
Thermal noise originates from the random Brownian motion of electrons in a resistive medium. Given a resistance, a bandwidth and a temperature, the rms thermal noise Vnt is given by:Vnt=√{square root over (4kbTΔfR)}where kb is Boltzmann's constant, 1,38065*10−23 J/K, T the absolute temperature in K, Δf the bandwidth of interest in Hz and R the resistance in Ω of the circuit element considered. For a MOS semiconductor, thermal noise En is given by:
      E    n    =            8      ·      k      ·      T              3      ·                        2          ·          K          ·                      I            d                    ·                      W            L                              where Id is the drain current, W is the physical width, and L is the physical length of the semiconductor element. A lower drain current will thus result in more input noise, but this is compensated by a higher signal level.
Flicker noise, or 1/f noise, is predominant in the noise spectrum at low frequencies. It has been observed in electronic devices since the era of vacuum tubes, and is also present in contemporary semiconductor devices. Since EEG signals typically are in the frequency range 0.1-100 Hz, it is important to limit 1/f noise as much as possible.
In order to provide an EEG monitoring system to be continuously carried by a person being monitored capable of working uninterrupted for several days without a need for replacing the battery, one design goal for the EEG monitoring system is that the current drawn from the battery by the electronic circuit is reduced as much as possible, preferably to a value below 1 mA. A semiconductor element providing amplification in the order of between one hundred times to perhaps a thousand times the signal present at its input uses a considerable percentage of this current as its bias current in order to handle the large gain within its operating limits.
In the case where the EEG monitoring system comprises two parts, e.g. an implantable part comprising electrodes and an external part comprising signal processing means and battery, the A/D converter will often be arranged with the electrodes in the implanted part. For this reason the power consumption of the A/D converter must be as low as possible. Often the internal implanted part will be prepared for receiving the necessary power from the external part. This could be achieved by the application of inductive means.
An EEG monitor adapted for being carried continuously by a person must be small and unobtrusive, and its power consumption has to be modest enough to allow for the use of light-weight batteries, which should be lasting at least a couple of days before needing replacement.
2. The Prior Art
Delta-sigma A/D converters are well known in the art. Their purpose is to convert a varying, analog input voltage into a binary bit stream for further processing in the digital domain. Delta-sigma A/D converters have significant advantages over other A/D converter designs. In order to reduce quantization noise introduced by the quantization stage (e.g. the comparator 3 shown in FIG. 1), oversampling and noise shaping is used. The oversampling and the delta-sigma modulator structure acts as a noise shaping filter, pushing the quantization noise from the frequency band of interest to higher frequencies as a consequence. Thereby a frequency band with a low noise figure is created for the signals of interest. A drawback is that the converter clock rate has to be higher than a traditional analog-to-digital converter operating at a sampling rate two times the highest frequency of interest, denoted the Nyquist limit. In delta-sigma converters oversampling ratios of 64 times to 128 times are often seen. However, this is a minor drawback in comparison with the advantages gained by the larger tolerance allowed for the values of the components in the converter.
In its essence, a delta-sigma A/D converter comprises a delta-sigma modulator and a low-pass filter. This may be made with an integrator, a comparator and a D-flip-flop. The output signal of the flip-flop is fed back through a feedback loop comprising a one-bit D/A converter, and is subtracted from the input signal upstream of the integrator. The subtracted feedback signal provides an error signal to the input of the delta-sigma modulator.
The error signal from the feedback loop of the A/D converter is used to ensure that, on average, the output signal level of the converter is always equal to the input signal level. If no signal is present on the converter input, a symmetric output bit stream of binary ones and zeroes is generated by the A/D converter. When the input signal voltage changes to a more positive voltage, more binary ones will be present in the output bit stream, and when the input signal voltage changes to a more negative voltage, more binary zeroes will be present in the output bit stream. The delta-sigma A/D converter thus converts an analog input signal into a balance between ones and zeroes in the output bit stream.